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Future of information and Communication Conference, FICC 2021

作者： Zaouch, Amal Benabbou, Faouzia Er-Raji, Naoufal Laboratory of Modeling and Information Technology Department of Computer Science and Mathematics Faculty of Sciences Ben M’Sik University Hassan II Casablanca Morocco

ISBN: (纸本)9783030731021

For a cloud datacenter, the biggest issue is how to handle millions of requests coming dynamically from the end users. To distribute requests efficiently and effectively, there is a need to balance the load evenly among the Cloud federations. In this paper, we propose a load balancing strategy and apply it on two levels control in a datacenter: physicals machines and clusters. The algorithm proposed is based on hierarchical strategy. Simulations on CloudSim are used to check its performance and its influence on standard deviation of load, communication cost and number of task migrations. Comparing with an algorithm, that does not include different levels in load balancing decision;results indicate that our method not only gives good Cloud balancing but also ensures optimizing Standard Deviation of load, resources utilization and communication cost by reducing number of migrations in all the datacenter. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System

arXiv

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arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Semantic Web for Sharing Medical Resources 6th

Semantic Web for Sharing Medical Resources

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6th International Conference on Business Intelligence, CBI 2021

作者： Meddaoui, Mohamed Amine Laboratory of Information Processing and Decision Support Department of Computer Science Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783030765071

The classical web is very helpful for using in different domain, especially in medicine. But still trying to improve results for this, we propose to use semantic web for sharing medical resources. In this article, we will discover the principle of the semantic web, which consists understanding the true meaning of the concepts in order to simplify the presentation of the data, which then serves to give the precise result during the research and the advantage of using the semantic web for sharing of medical resources. We use logical «Protege» to present the concepts and relation between them. And the limits that require innovation. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Web

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Identification of Complex Network Influencer using the Technology for Order Preference by Similarity to an Ideal Solution

Identification of Complex Network Influencer using the Techn...

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2020 International Conference on Mathematics and Data science, ICMDS 2020

作者： Ait Rai, K. Agouti, T. Machkour, M. Antari, J. Computer System and Vision Laboratory Faculty of Sciences Agadir Ibn Zohr University BP8106 Morocco Engineering and Information Systems Laboratory Faculty of Sciences Semlalia-Cadi Ayyad University Morocco Polydisciplinary Faculty Taroudant-Ibn Zohr University Morocco

Marketing through social networks is a recent approach which consists in using these networks to convince potential consumers with the quality of products or services offered by a company. Marketing is developing very quickly, particularly on Facebook, Twitter, LinkedIn, Instagram, YouTube, etc. The major advantage of social networks is the possibility of influencing a panel of people according to their interests but without having the feeling of being guided. Identifying influencers is an interesting topic in social networks, and centrality measures are among the methods used to address this topic. Each measure has some shortcomings. In this paper, we gather centrality measures by using Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, which is a Multi-Criteria Decision Making (MCDM) to identify potential influences in a social network. A case study is presented to explain carefully TOPSIS and to illustrate the effectiveness of the proposed method, three real datasets are used for the experiments. The results show that TOPSIS can rank spreaders more accurately than centrality criteria. © Published under licence by IOP Publishing Ltd.

关键词： Complex networks

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in Engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization

Full-Duplex Communication for ISAC: Joint Beamforming and Po...

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作者： He, Zhenyao Xu, Wei Shen, Hong Ng, Derrick Wing Kwan Eldar, Yonina C. You, Xiaohu Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China The University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication. © 1983-2012 IEEE.

关键词： Signal to noise ratio

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arXiv

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

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Model-based epidemic data reconstruction using feedback linearization

Model-based epidemic data reconstruction using feedback line...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Csutak, Balazs Polcz, Peter Szederkenyi, Gabor Pázmány Péter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, a model-based method is proposed for the reconstruction of non-measured epidemic data of the COVID-19 pandemic in Hungary. Only the data series showing the daily number of hospitalized people are used for the reconstruction together with a nonlinear dynamical model of epidemic spread containing 8 compartments. The unknown input of the model is the infection rate, which is computed through the solution of a feedback linearization-based asymptotic output tracking problem, where the reference is the actually observed number of hospitalized people. Computations show good match with of hospitalized people. Computations show good match with previous reconstruction results, and show a roughly 3.5-4-fold underdetection of infections until the Omicron wave. © 2022 IEEE.

关键词： Feedback linearization

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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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Research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal Dezhong Yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

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